About:
Massive Online Analysis (MOA) is a real time analytic tool for data streams. It is a software environment for implementing algorithms and running experiments for online learning from evolving data streams. MOA includes a collection of offline and online methods as well as tools for evaluation. In particular, it implements boosting, bagging, and Hoeffding Trees, all with and without Naive Bayes classifiers at the leaves. MOA supports bi-directional interaction with WEKA, the Waikato Environment for Knowledge Analysis, and it is released under the GNU GPL license.

Made LogNumber into a signed version of a log number and moved the prior
unsigned implementation into UnsignedLogNumber.

Added EuclideanRing interface that provides methods for times,
timesEquals, divide, and divideEquals. Also added Field interface that
provides methods for inverse and inverseEquals. These interfaces are now
implemented by the appropriate number classes such as ComplexNumber,
MutableInteger, MutableLong, MutableDouble, LogNumber, and
UnsignedLogNumber.

Added interface for Indexer and DefaultIndexer implementation for
creating a zero-based indexing of values.

Added interfaces for MatrixFactoryContainer and
DivergenceFunctionContainer.

Added ReversibleEvaluator, which various identity functions implement as
well as a new utility class ForwardReverseEvaluatorPair to create a
reversible evaluator from a pair of other evaluators.

Added method to create an ArrayList from a pair of values in
CollectionUtil.

ArgumentChecker now properly throws assertion errors for NaN values.
Also added checks for long types.

Fixed handling of Infinity in subtraction for LogMath.

Fixed issue with angle method that would cause a NaN if cosine had a
rounding error.

Added new createMatrix methods to MatrixFactory that initializes the
Matrix with the given value.

Added copy, reverse, and isEmpty methods for several array types to
ArrayUtil.

Added utility methods for creating a HashMap, LinkedHashMap, HashSet, or
LinkedHashSet with an expected size to CollectionUtil.

Added getFirst and getLast methods for List types to CollectionUtil.

Removed some calls to System.out and Exception.printStackTrace.

Common Data:

Added create method for IdentityDataConverter.

ReversibleDataConverter now is an extension of ReversibleEvaluator.

Learning Core:

Added general learner transformation capability to make it easier to adapt
and compose algorithms. InputOutputTransformedBatchLearner provides this
capability for supervised learning algorithms by composing together a
triplet. CompositeBatchLearnerPair does it for a pair of algorithms.

Added a constant and identity learners.

Added Chebyshev, Identity, and Minkowski distance metrics.

Added methods to DatasetUtil to get the output values for a dataset and
to compute the sum of weights.

Made generics more permissive for supervised cost functions.

Added ClusterDistanceEvaluator for taking a clustering that encodes the
distance from an input value to all clusters and returns the result as a
vector.

Fixed potential round-off issue in decision tree splitter.

Added random subspace technique, implemented in RandomSubspace.

Separated functionality from LinearFunction into IdentityScalarFunction.
LinearFunction by default is the same, but has parameters that can change
the slope and offset of the function.

Default squashing function for GeneralizedLinearModel and
DifferentiableGeneralizedLinearModel is now a linear function instead of
an atan function.

Added a weighted estimator for the Poisson distribution.

Added Regressor interface for evaluators that are the output of
(single-output) regression learning algorithms. Existing such evaluators
have been updated to implement this interface.

Added support for regression ensembles including additive and averaging
ensembles with and without weights. Added a learner for regression bagging
in BaggingRegressionLearner.